面向大型语言模型隐喻理解的心理知情思维链提示
Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models
AI总结：
本文提出基于认知心理学理论的思维链提示方法，将概率模型结构引入大型语言模型，通过引导模型推断潜在变量及关系来提升隐喻释义选择任务的性能。
摘要：
语言理解的概率模型是研究人类语言使用的宝贵工具。然而，它们需要针对特定领域进行人工设计。相比之下，大型语言模型（LLMs）在跨越广泛领域的文本上进行训练，但它们缺乏概率模型的结构和可解释性。在本文中，我们使用思维链提示将概率模型的结构引入LLMs。我们在隐喻理解的案例中探索这种方法。我们的思维链提示引导语言模型推断潜在变量并推理它们之间的关系，从而为隐喻选择合适的释义。所选的潜在变量和关系受到认知心理学中隐喻理解理论的启发。我们将这些提示应用于GPT-3的两个最大版本，并表明它们可以提高释义选择任务的性能。
英文摘要：
Probabilistic models of language understanding are valuable tools for investigating human language use. However, they need to be hand-designed for a particular domain. In contrast, large language models (LLMs) are trained on text that spans a wide array of domains, but they lack the structure and interpretability of probabilistic models. In this paper, we use chain-of-thought prompts to introduce structures from probabilistic models into LLMs. We explore this approach in the case of metaphor understanding. Our chain-of-thought prompts lead language models to infer latent variables and reason about their relationships in order to choose appropriate paraphrases for metaphors. The latent variables and relationships chosen are informed by theories of metaphor understanding from cognitive psychology. We apply these prompts to the two largest versions of GPT-3 and show that they can improve performance in a paraphrase selection task.
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